Production AI is mostly the work nobody puts in the demo.

Data + reporting

Create one trusted business view from fragmented systems

Business problem

Teams were reconciling exports, definitions, and exceptions before they could trust the report or ask AI a useful question.

System built

Connected source systems, governed metric logic, deterministic reporting, and grounded access for AI analysis.

Document operations

Extract known documents without hiding the edge cases

Business problem

Manual document review slowed the process, while fully automated extraction would have created silent bad data.

System built

Structured extraction, validation against known rules, confidence thresholds, and human review for exceptions.

AI quality controls

Make changes to prompts, tools, and models testable

Business problem

A better model or prompt could improve one behavior while quietly breaking another important use case.

System built

Repeatable evaluation pipelines, versioned changes, quality gates, evidence, and rollback paths.

Operational response

Turn failures into an actionable queue—not a scavenger hunt

Business problem

When a workflow failed, people had to collect logs, context, ownership, and history before they could even begin resolving it.

System built

Automated evidence collection, exception routing, alerts, ownership, and a clear operator view of what needs attention.

Back-office automation

Replace copy-paste handoffs with connected systems

Business problem

Recurring work moved through spreadsheets, inboxes, and tribal knowledge, with one person responsible for keeping it together.

System built

APIs, scheduled and event-driven jobs, business rules, approvals, and monitoring around the complete workflow.